Troubleshooting AutoAI experiments
If your AutoAI experiment fails to run successfully, review some of these common problems and resolutions.
Notebook for an experiment with subsampling can fail generating predictions
If you do pipeline refinery to prepare the model, and the experiment uses subsampling of the data during training, you might encounter an “unknown class” error running a notebook saved from the experiment.
The problem stems from an unknown class not included in the training data set. The workaround is to use the entire dataset for training or recreate the subsampling used in the experiment.
To subsample the training data (before
fit()), provide sample size by number of rows or by fraction of the sample (as done in the experiment).
If number of records was used in subsampling settings, you can increase the value of
n. For example:
train_df = train_df.sample(n=1000)
If subsampling is represented as a fraction of the data set, increase the value of
frac. For example:
train_df = train_df.sample(frac=0.4, random_state=experiment_metadata['random_state'])
Pipeline creation fails for binary classification
AutoAI analyzes a subset of the data to determine the best fit for experiment type. If the sample data in the prediction column contains only two values, AutoAI recommends a binary classification experiment and applies the related algorithms. If the full data set contains more than two values in the prediction column, however, the binary classification will fail and you will get an error indicating AutoAI cannot create the pipelines.
In this case, manually change the experiment type from binary to either multiclass, for a defined set of values, or regression, for an unspecified set of values.
- Click the Reconfigure Experiment icon to edit the experiment settings.
- On the Prediction page of Experiment Settings, change the prediction type to the one that best matches the data in the prediction column.
- Save the changes and run the experiment again.
Creating a batch deployment for an AutoAI model
You can create a batch deployment for a saved AutoAI model, but the model must be trained using the current version of Cloud Pak for Data. If it was trained using an older version, run the experiment again and deploy the resulting saved model.